df = read_csv("data.csv")
data <- df %>%
mutate(
market_value_equity = market_to_book * book_value, # Market value of equity
total_assets = book_value, # Approximate total assets using book value
V = market_value_equity + (leverage * book_value) # Market value of assets = equity + debt
)Naive DD
Scoring, M2 Data Science for Social Sciences
1. Calculate the market value of assets (V)
The market value of assets is estimated as the sum of the market value of equity and total debt
2. Calculate total debt (D)
Total debt is derived using the leverage ratio and book value of assets
data <- data %>%
mutate(
D = leverage * book_value # Total debt
)3. Calculate the Naive Distance to Default (Naive DD)
Naive DD = 1 - (Debt / Market value of assets)
data <- data %>%
mutate(
naive_DD = 1 - (D / V)
)4. Select relevant columns to display or save the results
naive_DD_results <- data %>%
select(fyear, gvkey, market_value_equity, D, V, naive_DD)5. Merge with Y column and filter
naive_DD_results <- naive_DD_results %>%
left_join(data %>% select(fyear, gvkey, Y), by = c("fyear", "gvkey"))
naive_DD_filtered <- naive_DD_results %>%
filter(Y == 1)